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STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m-3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m-3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the 'operational analysis' level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson's correlations (r is <0.6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.
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http://dx.doi.org/10.1093/annweh/wxab057 | DOI Listing |
Nat Microbiol
September 2025
Division of Computational Pathology, Brigham and Women's Hospital, Boston, MA, USA.
Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled.
View Article and Find Full Text PDFJ Aging Stud
September 2025
Dean of Area Studies and Assistant Dean of Faculty, IES Abroad Barcelona (Spain) & Research Fellow, Aston University, UK. Electronic address:
This article explores the representation of female sexuality in later life through the lens of three contemporary Spanish films: La vida era eso (2020), Destello bravío (2021), and Mamacruz (2023). Drawing from feminist aging studies, film theory, and concepts such as haptic visuality and clitoral sexuality, the study challenges the patriarchal, ageist, and phallocentric narratives that have long shaped cultural understandings of older women's erotic lives. Through close readings of these films, the article demonstrates how they subvert the dominant heteronormative gaze by foregrounding sensory pleasure, autoeroticism, and the reawakening of desire in older women.
View Article and Find Full Text PDFJ Aging Stud
September 2025
Department of Literature and Art, Maastricht University, the Netherlands.
This article offers an anocritical reading of Girls5eva, a sitcom about a 1990s one-hit girl group trying to make a comeback. Building on scholarship into the representation of aging women in popular media and the music industry, our reading first addresses fuzzy boundaries between life stages and transgressions of the normalized life course. Second, we examine the discourse of girl power and its relationship to midlife transformation.
View Article and Find Full Text PDFJ Colloid Interface Sci
September 2025
Center for Innovative Materials and Architectures, Ho Chi Minh City 700000, Viet Nam; Vietnam National University, Ho Chi Minh City 700000, Viet Nam. Electronic address:
Organic nucleophile-assisted natural seawater electrolysis has emerged as a promising strategy for green hydrogen production by significantly reducing energy consumption. Among Ni-based electrocatalysts, NiMoO has drawn attention for its activity in both oxygen evolution reaction (OER) and urea oxidation reaction (UOR). However, its practical application is hindered by severe surface passivation, particularly at industrial current densities (e.
View Article and Find Full Text PDFNeural Netw
September 2025
Dept. of CSE, Konkuk University, Seoul, 05029, Republic of Korea. Electronic address:
Neural network compression problems have been extensively studied to overcome the limitations of compute-intensive deep learning models. Most of the state-of-the-art solutions in this context are based on network pruning that identify and remove unimportant weights, filters or channels. However, existing methods often lack actual speedup or require complex pruning criteria and additional training (fine-tuning) overhead.
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